Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study
arXiv:2606.00062v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-based RAG systems are fundamentally limited in their ability to capture the structured, multi-entity relationships that underpin financial market analysis. This paper presents a comprehensive comparative study of a novel two-hop Graph-RAG architecture versus a standard vector-only baseline for cross-entity financial sentiment analysis. Our system constructs a sentiment-weighted knowledge graph of 59
The proliferation of RAG systems coupled with the increasing complexity of financial data necessitates more sophisticated methods for AI grounding and analysis.
Improving AI's ability to interpret complex, multi-entity financial relationships can significantly enhance predictive analytics and investment strategies.
AI systems are moving beyond simple vector embeddings to incorporate sophisticated graph structures for better contextual understanding in specialized domains like finance.
- · AI-driven financial analysis platforms
- · Quantitative hedge funds
- · Financial data providers
- · Traditional vector-only RAG systems
- · Manual financial analysts
More accurate and nuanced sentiment analysis in financial markets.
Better informed algorithmic trading strategies and risk assessments.
Potential for new financial instruments and market dynamics based on AI's enhanced interpretative capabilities.
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